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. 2021 Apr 15;11:8211. doi: 10.1038/s41598-021-87614-z

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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PC1 and PC2 of the trial components in each subject and the decision boundaries estimated by a classification tree algorithm. Horizontal and vertical axes indicate PC1 and PC2. Magenta, black, and cyan dots demonstrate the PC1 and PC2 values in each trial with 50%, 80%, and 100% effort, respectively. Magenta, black, and cyan areas denote the data area to be classified into 50%, 80%, and 100% effort estimated by the classification tree algorithm. Panels (A–H) denote the PC1 and PC2 values in each subject. Panel (C) surrounded by the black dotted rectangle corresponds to the typical subject in Fig. 3.